Methods, systems and apparatuses to enable an optimum bin selection by implementing a neural network with a network scheduling and configuration tool (NST), the method includes: configuring an agent with a critic function from neural networks wherein the agent neural network represents each bin of the collection of bins in the network that performs an action, and a critic function evaluates a criteria of success for performing the action; processing, by a scheduling algorithm, the VLs by the NST; determining one or more reward functions using global quality measurements based on criteria comprising: a lack of available bins, a lack of available VLs, and successfully scheduling operations of a VL into a bin; and training the network based on a normalized state model of the scheduled network by using input data sets to arrive at an optimum bin selection.
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2. The method of claim 1 wherein the optimum bin selection using a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) application to adjust a time slot selection algorithm policy for each bin.
7. The method of claim 6, wherein the goal is constrained by a set of factors including the lack of available bins, the packing efficiency of the bins, and the remaining time in the bins after scheduling.
13. The system of claim 12, wherein the optimum bin selection implements a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) application to adjust a time slot selection algorithm policy for each bin.
18. The system of claim 17, wherein the goal is constrained by a set of factors including the lack of available bins, the packing efficiency of the bins of the collection, and the remaining time in the bins after scheduling.
20. The apparatus of claim 19, wherein the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) application adjusts a time slot selection algorithm policy for each bin for optimum bin selection, and treats, via the MADDPG application, each bin as an independent agent to perform calculations of demand values at a beginning of a scheduling cycle over a duplicate set of global bins which represent an entire schedule cycle using a bin demand property on each bin of a global bin set.
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August 28, 2019
January 10, 2023
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